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arXiv · 2503.13991

GraphTEN: Graph Enhanced Texture Encoding Network

Bo Peng·Jintao Chen·Mufeng Yao·Chenhao Zhang·Jianghui Zhang·Mingmin Chi·Jiang Tao
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Abstract

Texture recognition is a fundamental problem in computer vision and pattern recognition. Recent progress leverages feature aggregation into discriminative descriptions based on convolutional neural networks (CNNs). However, modeling non-local context relations through visual primitives remains challenging due to the variability and randomness of texture primitives in spatial distributions. In this paper, we propose a graph-enhanced texture encoding network (GraphTEN) designed to capture both local and global features of texture primitives. GraphTEN models global associations through fully connected graphs and captures cross-scale dependencies of texture primitives via bipartite graphs. Additionally, we introduce a patch encoding module that utilizes a codebook to achieve an orderless representation of texture by encoding multi-scale patch features into a unified feature space. The proposed GraphTEN achieves superior performance compared to state-of-the-art methods across five publicly available datasets.

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Bo Peng, Jintao Chen, Mufeng Yao, Chenhao Zhang, Jianghui Zhang, Mingmin Chi, Jiang Tao. 2025-03-18. GraphTEN: Graph Enhanced Texture Encoding Network. https://arxiv.org/abs/2503.13991

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